Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis

Pan-transcriptome reveals a large accessory genome contribution to gene expression variation in yeast

Gene expression is an essential step in the translation of genotypes into phenotypes. However, little is known about the transcriptome architecture and the underlying genetic effects at the species level. Here we generated and analyzed the pan-transcriptome of ~1,000 yeast natural isolates across 4,977 core and 1,468 accessory genes. We found that the accessory genome is an underappreciated driver of transcriptome divergence. Global gene expression patterns combined with population structure showed that variation in heritable expression mainly lies within subpopulation-specific signatures, for which accessory genes are overrepresented. Genome-wide association analyses consistently highlighted that accessory genes are associated with proportionally more variants with larger effect sizes, illustrating the critical role of the accessory genome on the transcriptional landscape within and between populations.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Molecular mechanisms underpinning quantitative resistance to Phytophthora sojae in Glycine max using a systems genomics approach

Expression of quantitative disease resistance in many host-pathogen systems is controlled by genes at multiple loci, each contributing a small effect to the overall response. We used a systems genomics approach to study the molecular underpinnings of quantitative disease resistance in the soybean-Phytophthora sojae pathosystem, incorporating expression quantitative trait loci (eQTL) mapping and gene co-expression network analysis to identify the genes putatively regulating transcriptional changes in response to inoculation. These findings were compared to previously mapped phenotypic (phQTL) to identify the molecular mechanisms contributing to the expression of this resistance. A subset of 93 recombinant inbred lines (RILs) from a Conrad × Sloan population were inoculated with P. sojae isolate 1.S.1.1 using the tray-test method; RNA was extracted, sequenced, and the normalized read counts were genetically mapped from tissue collected at the inoculation site 24 h after inoculation from both mock and inoculated samples. In total, more than 100,000 eQTLs were mapped. There was a switch from predominantly cis-eQTLs in the mock treatment to an almost entirely nonoverlapping set of predominantly trans-eQTLs in the inoculated treatment, where greater than 100-fold more eQTLs were mapped relative to mock, indicating vast transcriptional reprogramming due to P. sojae infection occurred. The eQTLs were organized into 36 hotspots, with the four largest hotspots from the inoculated treatment corresponding to more than 70% of the eQTLs, each enriched for genes within plant-pathogen interaction pathways. Genetic regulation of trans-eQTLs in response to the pathogen was predicted to occur through transcription factors and signaling molecules involved in plant-pathogen interactions, plant hormone signal transduction, and MAPK pathways. Network analysis identified three co-expression modules that were correlated with susceptibility to P. sojae and associated with three eQTL hotspots. Among the eQTLs co-localized with phQTLs, two cis-eQTLs with putative functions in the regulation of root architecture or jasmonic acid, as well as the putative master regulators of an eQTL hotspot nearby a phQTL, represent candidates potentially underpinning the molecular control of these phQTLs for resistance.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Polygenic pathogen networks influence transcriptional plasticity in the Arabidopsis-Botrytis pathosystem

Bidirectional flow of information shapes the outcome of the host-pathogen interactions and depends on the genetics of each organism. Recent work has begun to use co-transcriptomic studies to shed light on this bidirectional flow, but it is unclear how plastic the co-transcriptome is in response to genetic variation in both the host and pathogen. To study co-transcriptome plasticity, we conducted transcriptomics using natural genetic variation in the pathogen, Botrytis cinerea, and large-effect genetic variation abolishing defense signaling pathways within the host, Arabidopsis thaliana. We show that genetic variation in the pathogen has a greater influence on the co-transcriptome than mutations that abolish defense signaling pathways in the host. Genome-wide association mapping using the pathogens' genetic variation and both organisms' transcriptomes allowed an assessment of how the pathogen modulates plasticity in response to the host. This showed that the differences in both organism's responses were linked to trans-expression quantitative trait loci (eQTL) hotspots within the pathogen's genome. These hotspots control gene sets in either the host or pathogen and show differential allele sensitivity to the host's genetic variation rather than qualitative host specificity. Interestingly, nearly all the trans-eQTL hotspots were unique to the host or pathogen transcriptomes. In this system of differential plasticity, the pathogen mediates the shift in the co-transcriptome more than the host.

A role for heritable transcriptomic variation in maize adaptation to temperate environments

Background

Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments.

Result

We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments.

Conclusion

Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions.

Supplementary information

The online version contains supplementary material available at 10.1186/s13059-023-02891-3.

Plasticity of maternal environment-dependent expression-QTLs of tomato seeds

Seeds are essential for plant reproduction, survival, and dispersal. Germination ability and successful establishment of young seedlings strongly depend on seed quality and on environmental factors such as nutrient availability. In tomato (Solanum lycopersicum) and many other species, seed quality and seedling establishment characteristics are determined by genetic variation, as well as the maternal environment in which the seeds develop and mature. The genetic contribution to variation in seed and seedling quality traits and environmental responsiveness can be estimated at transcriptome level in the dry seed by mapping genomic loci that affect gene expression (expression QTLs) in contrasting maternal environments. In this study, we applied RNA-sequencing to construct a linkage map and measure gene expression of seeds of a tomato recombinant inbred line (RIL) population derived from a cross between S. lycopersicum (cv. Moneymaker) and S. pimpinellifolium (G1.1554). The seeds matured on plants cultivated under different nutritional environments, i.e., on high phosphorus or low nitrogen. The obtained single-nucleotide polymorphisms (SNPs) were subsequently used to construct a genetic map. We show how the genetic landscape of plasticity in gene regulation in dry seeds is affected by the maternal nutrient environment. The combined information on natural genetic variation mediating (variation in) responsiveness to the environment may contribute to knowledge-based breeding programs aiming to develop crop cultivars that are resilient to stressful environments.

From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology

The selection of plant genotypes with improved productivity and tolerance to environmental constraints has always been a major concern in plant breeding. Classical approaches based on the generation of variability and selection of better phenotypes from large variant collections have improved their efficacy and processivity due to the implementation of molecular biology techniques, particularly genomics, Next Generation Sequencing and other omics such as proteomics and metabolomics. In this regard, the identification of interesting variants before they develop the phenotype trait of interest with molecular markers has advanced the breeding process of new varieties. Moreover, the correlation of phenotype or biochemical traits with gene expression or protein abundance has boosted the identification of potential new regulators of the traits of interest, using a relatively low number of variants. These important breakthrough technologies, built on top of classical approaches, will be improved in the future by including the spatial variable, allowing the identification of gene(s) involved in key processes at the tissue and cell levels.

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BACKGROUND

Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information.

METHODS

We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data.

RESULTS

Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses.

CONCLUSIONS

We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.

A consensus map for quality traits in durum wheat based on genome-wide association studies and detection of ortho-meta QTL across cereal species

The present work focused on the identification of durum wheat QTL hotspots from a collection of genome-wide association studies, for quality traits, such as grain protein content and composition, yellow color, fiber, grain microelement content (iron, magnesium, potassium, selenium, sulfur, calcium, cadmium), kernel vitreousness, semolina, and dough quality test. For the first time a total of 10 GWAS studies, comprising 395 marker-trait associations (MTA) on 57 quality traits, with more than 1,500 genotypes from 9 association panels, were used to investigate consensus QTL hotspots representative of a wide durum wheat genetic variation. MTA were found distributed on all the A and B genomes chromosomes with minimum number of MTA observed on chromosome 5B (15) and a maximum of 45 on chromosome 7A, with an average of 28 MTA per chromosome. The MTA were equally distributed on A (48%) and B (52%) genomes and allowed the identification of 94 QTL hotspots. Synteny maps for QTL were also performed in Zea mays, Brachypodium, and Oryza sativa, and candidate gene identification allowed the association of genes involved in biological processes playing a major role in the control of quality traits.

The multiple fates of gene duplications: Deletion, hypofunctionalization, subfunctionalization, neofunctionalization, dosage balance constraints, and neutral variation

Gene duplications have long been recognized as a contributor to the evolution of genes with new functions. Multiple copies of genes can result from tandem duplication, from transposition to new chromosomes, or from whole-genome duplication (polyploidy). The most common fate is that one member of the pair is deleted to return the gene to the singleton state. Other paths involve the reduced expression of both copies (hypofunctionalization) that are held in duplicate to maintain sufficient quantity of function. The two copies can split functions (subfunctionalization) or can diverge to generate a new function (neofunctionalization). Retention of duplicates resulting from doubling of the whole genome occurs for genes involved with multicomponent interactions such as transcription factors and signal transduction components. In contrast, these classes of genes are underrepresented in small segmental duplications. This complementary pattern suggests that the balance of interactors affects the fate of the duplicate pair. We discuss the different mechanisms that maintain duplicated genes, which may change over time and intersect.

The impact of species-wide gene expression variation on Caenorhabditis elegans complex traits

Phenotypic variation in organism-level traits has been studied in Caenorhabditis elegans wild strains, but the impacts of differences in gene expression and the underlying regulatory mechanisms are largely unknown. Here, we use natural variation in gene expression to connect genetic variants to differences in organismal-level traits, including drug and toxicant responses. We perform transcriptomic analyses on 207 genetically distinct C. elegans wild strains to study natural regulatory variation of gene expression. Using this massive dataset, we perform genome-wide association mappings to investigate the genetic basis underlying gene expression variation and reveal complex genetic architectures. We find a large collection of hotspots enriched for expression quantitative trait loci across the genome. We further use mediation analysis to understand how gene expression variation could underlie organism-level phenotypic variation for a variety of complex traits. These results reveal the natural diversity in gene expression and possible regulatory mechanisms in this keystone model organism, highlighting the promise of using gene expression variation to understand how phenotypic diversity is generated.

Comparative transcriptomics and eQTL mapping of response to Melampsora americana in selected Salix purpurea F2 progeny

Background

Melampsora spp. rusts are the greatest pathogen threat to shrub willow (Salix spp.) bioenergy crops. Genetic resistance is key to limit the effects of these foliar diseases on host response and biomass yield, however, the genetic basis of host resistance has not been characterized. The addition of new genomic resources for Salix provides greater power to investigate the interaction between S. purpurea and M. americana, species commonly found in the Northeast US. Here, we utilize 3′ RNA-seq to investigate host-pathogen interactions following controlled inoculations of M. americana on resistant and susceptible F₂S. purpurea genotypes identified in a recent QTL mapping study. Differential gene expression, network analysis, and eQTL mapping were used to contrast the response to inoculation and to identify associated candidate genes.

Results

Controlled inoculation in a replicated greenhouse study identified 19 and 105 differentially expressed genes between resistant and susceptible genotypes at 42 and 66 HPI, respectively. Defense response gene networks were activated in both resistant and susceptible genotypes and enriched for many of the same defense response genes, yet the hub genes of these common response modules showed greater mean expression among the resistant plants. Further, eight and six eQTL hotspots were identified at 42 and 66 HPI, respectively. The combined results of three analyses highlight 124 candidate genes in the host for further analysis while analysis of pathogen RNA showed differential expression of 22 genes, two of which are candidate pathogen effectors.

Conclusions

We identified two differentially expressed M. americana transcripts and 124 S. purpurea genes that are good candidates for future studies to confirm their role in conferring resistance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08254-1.

Both Binding Strength and Evolutionary Accessibility Affect the Population Frequency of Transcription Factor Binding Sequences in Arabidopsis thaliana

Mutations in DNA sequences that bind transcription factors and thus modulate gene expression are a source of adaptive variation in gene expression. To understand how transcription factor binding sequences evolve in natural populations of the thale cress Arabidopsis thaliana, we integrated genomic polymorphism data for loci bound by transcription factors with in vitro data on binding affinity for these transcription factors. Specifically, we studied 19 different transcription factors, and the allele frequencies of 8,333 genomic loci bound in vivo by these transcription factors in 1,135 A. thaliana accessions. We find that transcription factor binding sequences show very low genetic diversity, suggesting that they are subject to purifying selection. High frequency alleles of such binding sequences tend to bind transcription factors strongly. Conversely, alleles that are absent from the population tend to bind them weakly. In addition, alleles with high frequencies also tend to be the endpoints of many accessible evolutionary paths leading to these alleles. We show that both high affinity and high evolutionary accessibility contribute to high allele frequency for at least some transcription factors. Although binding sequences with stronger affinity are more frequent, we did not find them to be associated with higher gene expression levels. Epistatic interactions among individual mutations that alter binding affinity are pervasive and can help explain variation in accessibility among binding sequences. In summary, combining in vitro binding affinity data with in vivo binding sequence data can help understand the forces that affect the evolution of transcription factor binding sequences in natural populations.

Inheritance of gene expression throughout fruit development in chili pepper

Gene expression is the primary molecular phenotype and can be estimated in specific organs or tissues at particular times. Here we analyzed genome-wide inheritance of gene expression in fruits of chili pepper (Capsicum annuum L.) in reciprocal crosses between a domesticated and a wild accession, estimating this parameter during fruit development. We defined a general hierarchical schema to classify gene expression inheritance which can be employed for any quantitative trait. We found that inheritance of gene expression is affected by both, the time of fruit development as well as the direction of the cross, and propose that such variations could be common in many developmental processes. We conclude that classification of inheritance patterns is important to have a better understanding of the mechanisms underlying gene expression regulation, and demonstrate that sets of genes with specific inheritance pattern at particular times of fruit development are enriched in different biological processes, molecular functions and cell components. All curated data and functions for analysis and visualization are publicly available as an R package.

The genetics of gene expression in a Caenorhabditis elegans multiparental recombinant inbred line population

Studying genetic variation of gene expression provides a powerful way to unravel the molecular components underlying complex traits. Expression quantitative trait locus (eQTL) studies have been performed in several different model species, yet most of these linkage studies have been based on the genetic segregation of two parental alleles. Recently, we developed a multiparental segregating population of 200 recombinant inbred lines (mpRILs) derived from four wild isolates (JU1511, JU1926, JU1931, and JU1941) in the nematode Caenorhabditis elegans. We used RNA-seq to investigate how multiple alleles affect gene expression in these mpRILs. We found 1789 genes differentially expressed between the parental lines. Transgression, expression beyond any of the parental lines in the mpRILs, was found for 7896 genes. For expression QTL mapping almost 9000 SNPs were available. By combining these SNPs and the RNA-seq profiles of the mpRILs, we detected almost 6800 eQTLs. Most trans-eQTLs (63%) co-locate in six newly identified trans-bands. The trans-eQTLs found in previous two-parental allele eQTL experiments and this study showed some overlap (17.5–46.8%), highlighting on the one hand that a large group of genes is affected by polymorphic regulators across populations and conditions, on the other hand, it shows that the mpRIL population allows identification of novel gene expression regulatory loci. Taken together, the analysis of our mpRIL population provides a more refined insight into C. elegans complex trait genetics and eQTLs in general, as well as a starting point to further test and develop advanced statistical models for detection of multiallelic eQTLs and systems genetics studying the genotype–phenotype relationship.

Trans-acting genetic variation affects the expression of adjacent genes

Gene expression differences among individuals are shaped by trans-acting expression quantitative trait loci (eQTLs). Most trans-eQTLs map to hotspot locations that influence many genes. The molecular mechanisms perturbed by hotspots are often assumed to involve "vertical" cascades of effects in pathways that can ultimately affect the expression of thousands of genes. Here, we report that trans-eQTLs can affect the expression of adjacent genes via "horizontal" mechanisms that extend along a chromosome. Genes affected by trans-eQTL hotspots in the yeast Saccharomyces cerevisiae were more likely to be located next to each other than expected by chance. These paired hotspot effects tended to occur at adjacent genes that also show coexpression in response to genetic and environmental perturbations, suggesting shared mechanisms. Physical proximity and shared chromatin state, in addition to regulation of adjacent genes by similar transcription factors, were independently associated with paired hotspot effects among adjacent genes. Paired effects of trans-eQTLs can occur at neighboring genes even when these genes do not share a common function. This phenomenon could result in unexpected connections between regulatory genetic variation and phenotypes.

A statistical framework for QTL hotspot detection

Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These methods may suffer from the problems of ignoring the correlation structure among traits, neglecting the magnitude of LOD scores for the QTL, or paying a very high computational cost, which often lead to the detection of excessive spurious hotspots, failure to discover biologically interesting hotspots composed of a small-to-moderate number of QTL with strong LOD scores, and computational intractability, respectively, during the detection process. In this article, we describe a statistical framework that can handle both types of data as well as address all the problems at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computational cost and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top γn,α profile, are introduced into the framework. The trait grouping attempts to group the traits controlled by closely linked or pleiotropic QTL together into the same trait groups and randomly allocates these QTL together across the genomic positions separately by trait group to account for the correlation structure among traits, so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top γn,α profile is designed to outline the LOD-score pattern of QTL in a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD-score distributions. Real examples, numerical analysis, and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots, and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.

Rare variants regulate expression of nearby individual genes in multiple tissues

The rapid decrease in sequencing cost has enabled genetic studies to discover rare variants associated with complex diseases and traits. Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how the variants influence diseases. Similar to the hypothesis of common variants, rare variants may affect diseases by regulating gene expression, and recently, several studies have identified the effects of rare variants on gene expression using heritability and expression outlier analyses. However, identifying individual genes whose expression is regulated by rare variants has been challenging due to the relatively small sample size of expression quantitative trait loci studies and statistical approaches not optimized to detect the effects of rare variants. In this study, we analyze whole-genome sequencing and RNA-seq data of 681 European individuals collected for the Genotype-Tissue Expression (GTEx) project (v8) to identify individual genes in 49 human tissues whose expression is regulated by rare variants. To improve statistical power, we develop an approach based on a likelihood ratio test that combines effects of multiple rare variants in a nonlinear manner and has higher power than previous approaches. Using GTEx data, we identify many genes regulated by rare variants, and some of them are only regulated by rare variants and not by common variants. We also find that genes regulated by rare variants are enriched for expression outliers and disease-causing genes. These results suggest the regulatory effects of rare variants, which would be important in interpreting associations of rare variants with complex traits.

It has been shown that rare variants may affect many diseases including both rare and common diseases with the advent of next-generation sequencing technology. An important question is how rare variants affect diseases or traits, especially whether or how they regulate gene expression as they may affect diseases through gene regulation. However, it is challenging to identify the regulatory effects of rare variants because it often requires large sample sizes and the existing statistical approaches are not optimized for it. Here, we develop a novel method, LRT-q, based on a likelihood ratio test that aggregates the effects of multiple rare variants nonlinearly to achieve higher statistical power than previous rare variant association methods. We apply LRT-q to the latest GTEx v8 dataset and identify regulatory effect of rare variants on individual genes. We also observe that genes regulated by rare variants are likely to be disease-causing genes. These results demonstrate the functional effects of rare variants, especially on gene expression, which provides important biological insights in understanding the genetic mechanism of rare variants in complex traits and diseases.

Chromosomal characteristics of salt stress heritable gene expression in the rice genome

BACKGROUND

Gene expression is potentially an important heritable quantitative trait that mediates between genetic variation and higher-level complex phenotypes through time and condition-dependent regulatory interactions. Therefore, we sought to explore both the genomic and condition-specific characteristics of gene expression heritability within the context of chromosomal structure.

RESULTS

Heritability was estimated for biological gene expression using a diverse, 84-line, Oryza sativa (rice) population under optimal and salt-stressed conditions. Overall, 5936 genes were found to have heritable expression regardless of condition and 1377 genes were found to have heritable expression only during salt stress. These genes with salt-specific heritable expression are enriched for functional terms associated with response to stimulus and transcription factor activity. Additionally, we discovered that highly and lowly expressed genes, and genes with heritable expression are distributed differently along the chromosomes in patterns that follow previously identified high-throughput chromosomal conformation capture (Hi-C) A/B chromatin compartments. Furthermore, multiple genomic hot-spots enriched for genes with salt-specific heritability were identified on chromosomes 1, 4, 6, and 8. These hotspots were found to contain genes functionally enriched for transcriptional regulation and overlaps with a previously identified major QTL for salt-tolerance in rice.

CONCLUSIONS

Investigating the heritability of traits, and in-particular gene expression traits, is important towards developing a basic understanding of how regulatory networks behave across a population. This work provides insights into spatial patterns of heritable gene expression at the chromosomal level.

Fine mapping identifies NAD-ME1 as a candidate underlying a major locus controlling temporal variation in primary and specialized metabolism in Arabidopsis

Plant metabolism is modulated by a complex interplay between internal signals and external cues. A major goal of all quantitative metabolomic studies is to clone the underlying genes to understand the mechanistic basis of this variation. Using fine-scale genetic mapping, in this work we report the identification and initial characterization of NAD-DEPENDENT MALIC ENZYME 1 (NAD-ME1) as the candidate gene underlying the pleiotropic network Met.II.15 quantitative trait locus controlling variation in plant metabolism and circadian clock outputs in the Bay × Sha Arabidopsis population. Transcript abundance and promoter analysis in NAD-ME1^Bay-0 and NAD-ME1^Sha alleles confirmed allele-specific expression that appears to be due a polymorphism disrupting a putative circadian cis-element binding site. Analysis of transfer DNA insertion lines and heterogeneous inbred families showed that transcript variation of the NAD-ME1 gene led to temporal shifts of tricarboxylic acid cycle intermediates, glucosinolate (GSL) accumulation, and altered regulation of several GSL biosynthesis pathway genes. Untargeted metabolomic analyses revealed complex regulatory networks of NAD-ME1 dependent upon the daytime. The mutant led to shifts in plant primary metabolites, cell wall components, isoprenoids, fatty acids, and plant immunity phytochemicals, among others. Our findings suggest that NAD-ME1 may act as a key gene to coordinate plant primary and secondary metabolism in a time-dependent manner.

In silico Analysis of Polymorphisms in microRNAs Deregulated in Alzheimer Disease

BACKGROUND

Alzheimer's disease (AD) is a degenerative condition characterized by progressive cognitive impairment and dementia. Findings have revolutionized current knowledge of miRNA in the neurological conditions. Two regulatory mechanisms determine the level of mature miRNA expression; one is miRNA precursor processing, and the other is gene expression regulation by transcription factors. This study is allocated to the in-silico investigation of miRNA's SNPs and their effect on other cell mechanisms.

METHODS

We used databases which annotate the functional effect of SNPs on mRNA-miRNA and miRNA-RBP interaction. Also, we investigated SNPs which are located on the promoter or UTR region.

RESULTS

miRNA SNP3.0 database indicated several SNPs in miR-339 and miR-34a in the upstream and downstream of pre-miRNA and mature miRNAs. While, for some miRNAs miR-124, and miR-125, no polymorphism was observed, and also miR-101 with ΔG -3.1 and mir-328 with ΔG 5.8 had the highest and lowest potencies to produce mature microRNA. SNP2TFBS web-server presented several SNPs which altered the Transcription Factor Binding Sites (TFBS) or generated novel TFBS in the promoter regions of related miRNA. At last, RBP-Var database provided a list of SNPs which alter miRNA-RBP interaction pattern and can also influence other miRNAs' expression.

DISCUSSION

The results indicated that SNPs microRNA affects both miRNA function and miRNA expression. Our study expands molecular insight into how SNPs in different parts of miRNA, including the regulatory (promoter), the precursor (pre-miRNA), functional regions (seed region of mature miRNA), and RBP-binding motifs, which theoretically may be correlated to the Alzheimer's disease.

Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation

Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.

Systematic identification of cis-regulatory variants that cause gene expression differences in a yeast cross

Sequence variation in regulatory DNA alters gene expression and shapes genetically complex traits. However, the identification of individual, causal regulatory variants is challenging. Here, we used a massively parallel reporter assay to measure the cis-regulatory consequences of 5832 natural DNA variants in the promoters of 2503 genes in the yeast Saccharomyces cerevisiae. We identified 451 causal variants, which underlie genetic loci known to affect gene expression. Several promoters harbored multiple causal variants. In five promoters, pairs of variants showed non-additive, epistatic interactions. Causal variants were enriched at conserved nucleotides, tended to have low derived allele frequency, and were depleted from promoters of essential genes, which is consistent with the action of negative selection. Causal variants were also enriched for alterations in transcription factor binding sites. Models integrating these features provided modest, but statistically significant, ability to predict causal variants. This work revealed a complex molecular basis for cis-acting regulatory variation.

Network Analysis Prioritizes DEWAX and ICE1 as the Candidate Genes for Major eQTL Hotspots in Seed Germination of Arabidopsis thaliana

Seed germination is characterized by a constant change of gene expression across different time points. These changes are related to specific processes, which eventually determine the onset of seed germination. To get a better understanding on the regulation of gene expression during seed germination, we performed a quantitative trait locus mapping of gene expression (eQTL) at four important seed germination stages (primary dormant, after-ripened, six-hour after imbibition, and radicle protrusion stage) using Arabidopsis thaliana Bay x Sha recombinant inbred lines (RILs). The mapping displayed the distinctness of the eQTL landscape for each stage. We found several eQTL hotspots across stages associated with the regulation of expression of a large number of genes. Interestingly, an eQTL hotspot on chromosome five collocates with hotspots for phenotypic and metabolic QTL in the same population. Finally, we constructed a gene co-expression network to prioritize the regulatory genes for two major eQTL hotspots. The network analysis prioritizes transcription factors DEWAX and ICE1 as the most likely regulatory genes for the hotspot. Together, we have revealed that the genetic regulation of gene expression is dynamic along the course of seed germination.

Large-scale identification of expression quantitative trait loci in Arabidopsis reveals novel candidate regulators of immune responses and other processes

The extensive phenotypic diversity within natural populations of Arabidopsis is associated with differences in gene expression. Transcript levels can be considered as inheritable quantitative traits, and used to map expression quantitative trait loci (eQTL) in genome-wide association studies (GWASs). In order to identify putative genetic determinants for variations in gene expression, we used publicly available genomic and transcript variation data from 665 Arabidopsis accessions and applied the single nucleotide polymorphism-set (Sequence) Kernel Association Test (SKAT) method for the identification of eQTL. Moreover, we used the penalized orthogonal-components regression (POCRE) method to increase the power of statistical tests. Then, gene annotations were used as test units to identify genes that are associated with natural variations in transcript accumulation, which correspond to candidate regulators, some of which may have a broad impact on gene expression. Besides increasing the chances to identify real associations, the analysis using POCRE and SKAT significantly reduced the computational cost required to analyze large datasets. As a proof of concept, we used this approach to identify eQTL that represent novel candidate regulators of immune responses. The versatility of this approach allows its application to any process that is subjected to natural variation among Arabidopsis accessions.

Detecting selection with a genetic cross

Distinguishing which traits have evolved under natural selection, as opposed to neutral evolution, is a major goal of evolutionary biology. Several tests have been proposed to accomplish this, but these either rely on false assumptions or suffer from low power. Here, I introduce an approach to detecting selection that makes minimal assumptions and only requires phenotypic data from ∼10 individuals. The test compares the phenotypic difference between two populations to what would be expected by chance under neutral evolution, which can be estimated from the phenotypic distribution of an F2 cross between those populations. Simulations show that the test is robust to variation in the number of loci affecting the trait, the distribution of locus effect sizes, heritability, dominance, and epistasis. Comparing its performance to the QTL sign test-an existing test of selection that requires both genotype and phenotype data-the new test achieves comparable power with 50- to 100-fold fewer individuals (and no genotype data). Applying the test to empirical data spanning over a century shows strong directional selection in many crops, as well as on naturally selected traits such as head shape in Hawaiian Drosophila and skin color in humans. Applied to gene expression data, the test reveals that the strength of stabilizing selection acting on mRNA levels in a species is strongly associated with that species' effective population size. In sum, this test is applicable to phenotypic data from almost any genetic cross, allowing selection to be detected more easily and powerfully than previously possible.

Xylem systems genetics analysis reveals a key regulator of lignin biosynthesis in Populus deltoides

Despite the growing resources and tools for high-throughput characterization and analysis of genomic information, the discovery of the genetic elements that regulate complex traits remains a challenge. Systems genetics is an emerging field that aims to understand the flow of biological information that underlies complex traits from genotype to phenotype. In this study, we used a systems genetics approach to identify and evaluate regulators of the lignin biosynthesis pathway in Populus deltoides by combining genome, transcriptome, and phenotype data from a population of 268 unrelated individuals of P. deltoides The discovery of lignin regulators began with the quantitative genetic analysis of the xylem transcriptome and resulted in the detection of 6706 and 4628 significant local- and distant-eQTL associations, respectively. Among the locally regulated genes, we identified the R2R3-MYB transcription factor MYB125 (Potri.003G114100) as a putative trans-regulator of the majority of genes in the lignin biosynthesis pathway. The expression of MYB125 in a diverse population positively correlated with lignin content. Furthermore, overexpression of MYB125 in transgenic poplar resulted in increased lignin content, as well as altered expression of genes in the lignin biosynthesis pathway. Altogether, our findings indicate that MYB125 is involved in the control of a transcriptional coexpression network of lignin biosynthesis genes during secondary cell wall formation in P. deltoides.

Combined GWAS and eQTL analysis uncovers a genetic regulatory network orchestrating the initiation of secondary cell wall development in cotton

The cotton fibre serves as a valuable experimental system to study cell wall synthesis in plants, but our understanding of the genetic regulation of this process during fibre development remains limited. We performed a genome-wide association study (GWAS) and identified 28 genetic loci associated with fibre quality in allotetraploid cotton. To investigate the regulatory roles of these loci, we sequenced fibre transcriptomes of 251 cotton accessions and identified 15 330 expression quantitative trait loci (eQTL). Analysis of local eQTL and GWAS data prioritised 13 likely causal genes for differential fibre quality in a transcriptome-wide association study (TWAS). Characterisation of distal eQTL revealed unequal genetic regulation patterns between two subgenomes, highlighted by an eQTL hotspot (Hot216) that established a genome-wide genetic network regulating the expression of 962 genes. The primary regulatory role of Hot216, and specifically the gene encoding a KIP-related protein, was found to be the transcriptional regulation of genes responsible for cell wall synthesis, which contributes to fibre length by modulating the developmental transition from rapid cell elongation to secondary cell wall synthesis. This study uncovered the genetic regulation of fibre-cell development and revealed the molecular basis of the temporal modulation of secondary cell wall synthesis during plant cell elongation.

The contribution of cis- and trans-acting variants to gene regulation in wild and domesticated barley under cold stress and control conditions

Barley, like other crops, has experienced a series of genetic changes that have impacted its architecture and growth habit to suit the needs of humans, termed the domestication syndrome. Domestication also resulted in a concomitant bottleneck that reduced sequence diversity in genes and regulatory regions. Little is known about regulatory changes resulting from domestication in barley. We used RNA sequencing to examine allele-specific expression in hybrids between wild and domesticated barley. Our results show that most genes have conserved regulation. In contrast to studies of allele-specific expression in interspecific hybrids, we find almost a complete absence of trans effects. We also find that cis regulation is largely stable in response to short-term cold stress. Our study has practical implications for crop improvement using wild relatives. Genes regulated in cis are more likely to be expressed in a new genetic background at the same level as in their native background.

Pathogen Genetic Control of Transcriptome Variation in the Arabidopsis thaliana - Botrytis cinerea Pathosystem

In plant-pathogen relations, disease symptoms arise from the interaction of the host and pathogen genomes. Host-pathogen functional gene interactions are well described, whereas little is known about how the pathogen genetic variation modulates both organisms' transcriptomes. To model and generate hypotheses on a generalist pathogen control of gene expression regulation, we used the Arabidopsis thaliana-Botrytis cinerea pathosystem and the genetic diversity of a collection of 96 B. cinerea isolates. We performed expression-based genome-wide association (eGWA) for each of 23,947 measurable transcripts in Arabidopsis (host), and 9267 measurable transcripts in B. cinerea (pathogen). Unlike other eGWA studies, we detected a relative absence of locally acting expression quantitative trait loci (cis-eQTL), partly caused by structural variants and allelic heterogeneity hindering their identification. This study identified several distantly acting trans-eQTL linked to eQTL hotspots dispersed across Botrytis genome that altered only Botrytis transcripts, only Arabidopsis transcripts, or transcripts from both species. Gene membership in the trans-eQTL hotspots suggests links between gene expression regulation and both known and novel virulence mechanisms in this pathosystem. Genes annotated to these hotspots provide potential targets for blocking manipulation of the host response by this ubiquitous generalist necrotrophic pathogen.

The Evolutionary Forces Shaping Cis- and Trans-Regulation of Gene Expression within a Population of Outcrossing Plants

Understanding the persistence of genetic variation within populations has long been a goal of evolutionary biology. One promising route toward achieving this goal is using population genetic approaches to describe how selection acts on the loci associated with trait variation. Gene expression provides a model trait for addressing the challenge of the maintenance of variation because it can be measured genome-wide without information about how gene expression affects traits. Previous work has shown that loci affecting the expression of nearby genes (local or cis-eQTLs) are under negative selection, but we lack a clear understanding of the selective forces acting on variants that affect the expression of genes in trans. Here, we identify loci that affect gene expression in trans using genomic and transcriptomic data from one population of the obligately outcrossing plant, Capsella grandiflora. The allele frequencies of trans-eQTLs are consistent with stronger negative selection acting on trans-eQTLs than cis-eQTLs, and stronger negative selection acting on trans-eQTLs associated with the expression of multiple genes. However, despite this general pattern, we still observe the presence of a trans-eQTL at intermediate frequency that affects the expression of a large number of genes in the same coexpression module. Overall, our work highlights the different selective pressures shaping variation in cis- and trans-regulation.

The Role of Transcriptional Regulation in Hybrid Vigor

The genetic basis of hybrid vigor in plants remains largely unsolved but strong evidence suggests that variation in transcriptional regulation can explain many aspects of this phenomenon. Natural variation in transcriptional regulation is highly abundant in virtually all species and thus a potential source of heterotic variability. Allele Specific Expression (ASE), which is tightly linked to parent of origin effects and modulated by complex interactions in cis and in trans, is generally considered to play a key role in explaining the differences between hybrids and parental lines. Here we discuss the recent developments in elucidating the role of transcriptional variation in a number of aspects of hybrid vigor, thereby bridging old paradigms and hypotheses with contemporary research in various species.

Genome-wide expression quantitative trait locus analysis in a recombinant inbred line population for trait dissection in peanut

The transcriptome connects genome to the gene function and ultimate phenome in biology. So far, transcriptomic approach was not used in peanut for performing trait mapping in bi-parental populations. In this research, we sequenced the whole transcriptome in immature seeds in a peanut recombinant inbred line (RIL) population and explored thoroughly the landscape of transcriptomic variations and its genetic basis. The comprehensive analysis identified total 49 691 genes in RIL population, of which 92 genes followed a paramutation-like expression pattern. Expression quantitative trait locus (eQTL) analysis identified 1207 local eQTLs and 15 837 distant eQTLs contributing to the whole-genome transcriptomic variation in peanut. There were 94 eQTL hot spot regions detected across the genome with the dominance of distant eQTL. By integrating transcriptomic profile and annotation analyses, we unveiled a putative candidate gene and developed a linked marker InDel02 underlying a major QTL responsible for purple testa colour in peanut. Our result provided a first understanding of genetic basis of whole-genome transcriptomic variation in peanut and illustrates the potential of the transcriptome-aid approach in dissecting important traits in non-model plants.

WormQTL2: an interactive platform for systems genetics in Caenorhabditis elegans

Quantitative genetics provides the tools for linking polymorphic loci to trait variation. Linkage analysis of gene expression is an established and widely applied method, leading to the identification of expression quantitative trait loci (eQTLs). (e)QTL detection facilitates the identification and understanding of the underlying molecular components and pathways, yet (e)QTL data access and mining often is a bottleneck. Here, we present WormQTL2, a database and platform for comparative investigations and meta-analyses of published (e)QTL data sets in the model nematode worm C. elegans. WormQTL2 integrates six eQTL studies spanning 11 conditions as well as over 1000 traits from 32 studies and allows experimental results to be compared, reused and extended upon to guide further experiments and conduct systems-genetic analyses. For example, one can easily screen a locus for specific cis-eQTLs that could be linked to variation in other traits, detect gene-by-environment interactions by comparing eQTLs under different conditions, or find correlations between QTL profiles of classical traits and gene expression. WormQTL2 makes data on natural variation in C. elegans and the identified QTLs interactively accessible, allowing studies beyond the original publications. Database URL: www.bioinformatics.nl/WormQTL2/.

Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development

Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use hierarchical Bayesian Function-Valued Trait (FVT) models to estimate Brassica rapa growth curves throughout ontogeny, across two treatments, and in two growing seasons. We find genetic variation for plasticity of growth rates and final sizes, but not the inflection point (transition from accelerating to decelerating growth) of growth curves. There are trade-offs between growth rate and duration, indicating that selection for maximum yields at early harvest dates may come at the expense of late harvest yields and vice versa. We generate eigengene modules and determine which are co-expressed with FVT traits using a Weighted Gene Co-expression Analysis. Independently, we seed a Mutual Rank co-expression network model with FVT traits to identify specific genes and gene networks related to FVT. GO-analyses of eigengene modules indicate roles for actin/cytoskeletal genes, herbivore resistance/wounding responses, and cell division, while MR networks demonstrate a close association between metabolic regulation and plant growth. We determine that combining FVT Quantitative Trait Loci (QTL) and MR genes/WGCNA eigengene expression profiles better characterizes phenotypic variation than any single data type (i.e. QTL, gene, or eigengene alone). Our network analysis allows us to employ a targeted eQTL analysis, which we use to identify regulatory hotspots for FVT. We examine cis vs. trans eQTL that mechanistically link FVT QTL with structural trait variation. Colocalization of FVT, gene, and eigengene eQTL provide strong evidence for candidate genes influencing plant height. The study is the first to explore eQTL for FVT, and specifically do so in agroecologically relevant field settings.

We estimate the developmental dynamics of plant growth using mathematical functions to fit continuous functions to discrete plant height data collected throughout growth, and we use the parameters defining these mathematical functions as data. We identify genomic regions controlling plant growth and filter a novel transcriptomic data set using network reconstruction models to identify the genes and eigengenes associated with plant height. We combine these genomic and transcriptomic data to predict variation in plant height, and we use quantitative genetics to mechanistically connect plant genetics, transcriptomics, and development. Our approach demonstrates two powerful methods for the type of data reduction (FVT modeling and gene expression network reconstruction for targeted eQTL analyses) and data integration that will be necessary for driving forward the field of genetics in the post-genomic era. To the best of our knowledge, we are the first to apply these techniques to continuous models of plant development, and the first to do so in agroecologically relevant field settings.

Mechanistic interpretation of non-coding variants for discovering transcriptional regulators of drug response

BACKGROUND

Identification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples allow functional annotation of the genome, but do not provide ready answers regarding the effects of non-coding variants on phenotypes. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant's impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover transcription factors regulating phenotypic variation among individuals.

RESULTS

We developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq, and Hi-C chromatin interaction data to answer this question. Using drug sensitivity of lymphoblastoid cell lines as the phenotype of interest, we tested if non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA binding strength of a TF and thus identified TFs regulating individual differences in the phenotype. Our approach relies on a new method for predicting variant impact on TF-DNA binding that uses a combination of biophysical modeling and machine learning. We report statistical and literature-based support for many of the TFs discovered here as regulators of drug response variation. We show that the use of mechanistically driven variant impact predictors can identify TF-drug associations that would otherwise be missed. We examined in depth one reported association-that of the transcription factor ELF1 with the drug doxorubicin-and identified several genes that may mediate this regulatory relationship.

CONCLUSION

Our work represents initial steps in utilizing predictions of variant impact on TF binding sites for discovery of regulatory mechanisms underlying phenotypic variation. Future advances on this topic will be greatly beneficial to the reconstruction of phenotype-associated gene regulatory networks.

A Very Oil Yellow1 Modifier of the Oil Yellow1-N1989 Allele Uncovers a Cryptic Phenotypic Impact of Cis-regulatory Variation in Maize

Forward genetics determines the function of genes underlying trait variation by identifying the change in DNA responsible for changes in phenotype. Detecting phenotypically-relevant variation outside protein coding sequences and distinguishing this from neutral variants is not trivial; partly because the mechanisms by which DNA polymorphisms in the intergenic regions affect gene regulation are poorly understood. Here we utilized a dominant genetic reporter to investigate the effect of cis and trans-acting regulatory variation. We performed a forward genetic screen for natural variation that suppressed or enhanced the semi-dominant mutant allele Oy1-N1989, encoding the magnesium chelatase subunit I of maize. This mutant permits rapid phenotyping of leaf color as a reporter for chlorophyll accumulation, and mapping of natural variation in maize affecting chlorophyll metabolism. We identified a single modifier locus segregating between B73 and Mo17 that was linked to the reporter gene itself, which we call very oil yellow1 (vey1). Based on the variation in OY1 transcript abundance and genome-wide association data, vey1 is predicted to consist of multiple cis-acting regulatory sequence polymorphisms encoded at the wild-type oy1 alleles. The vey1 locus appears to be a common polymorphism in the maize germplasm that alters the expression level of a key gene in chlorophyll biosynthesis. These vey1 alleles have no discernable impact on leaf chlorophyll in the absence of the Oy1-N1989 reporter. Thus, the use of a mutant as a reporter for magnesium chelatase activity resulted in the detection of expression-level polymorphisms not readily visible in the laboratory.

A Statistical Procedure for Genome-Wide Detection of QTL Hotspots Using Public Databases with Application to Rice

Genome-wide detection of quantitative trait loci (QTL) hotspots underlying variation in many molecular and phenotypic traits has been a key step in various biological studies since the QTL hotspots are highly informative and can be linked to the genes for the quantitative traits. Several statistical methods have been proposed to detect QTL hotspots. These hotspot detection methods rely heavily on permutation tests performed on summarized QTL data or individual-level data (with genotypes and phenotypes) from the genetical genomics experiments. In this article, we propose a statistical procedure for QTL hotspot detection by using the summarized QTL (interval) data collected in public web-accessible databases. First, a simple statistical method based on the uniform distribution is derived to convert the QTL interval data into the expected QTL frequency (EQF) matrix. And then, to account for the correlation structure among traits, the QTL for correlated traits are grouped together into the same categories to form a reduced EQF matrix. Furthermore, a permutation algorithm on the EQF elements or on the QTL intervals is developed to compute a sliding scale of EQF thresholds, ranging from strict to liberal, for assessing the significance of QTL hotspots. With grouping, much stricter thresholds can be obtained to avoid the detection of spurious hotspots. Real example analysis and simulation study are carried out to illustrate our procedure, evaluate the performances and compare with other methods. It shows that our procedure can control the genome-wide error rates at the target levels, provide appropriate thresholds for correlated data and is comparable to the methods using individual-level data in hotspot detection. Depending on the thresholds used, more than 100 hotspots are detected in GRAMENE rice database. We also perform a genome-wide comparative analysis of the detected hotspots and the known genes collected in the Rice Q-TARO database. The comparative analysis reveals that the hotspots and genes are conformable in the sense that they co-localize closely and are functionally related to relevant traits. Our statistical procedure can provide a framework for exploring the networks among QTL hotspots, genes and quantitative traits in biological studies. The R codes that produce both numerical and graphical outputs of QTL hotspot detection in the genome are available on the worldwide web http://www.stat.sinica.edu.tw/chkao/.

A 24 h Age Difference Causes Twice as Much Gene Expression Divergence as 100 Generations of Adaptation to a Novel Environment

Gene expression profiling is one of the most reliable high-throughput phenotyping methods, allowing researchers to quantify the transcript abundance of expressed genes. Because many biotic and abiotic factors influence gene expression, it is recommended to control them as tightly as possible. Here, we show that a 24 h age difference of Drosophilasimulans females that were subjected to RNA sequencing (RNA-Seq) five and six days after eclosure resulted in more than 2000 differentially expressed genes. This is twice the number of genes that changed expression during 100 generations of evolution in a novel hot laboratory environment. Importantly, most of the genes differing in expression due to age introduce false positives or negatives if an adaptive gene expression analysis is not controlled for age. Our results indicate that tightly controlled experimental conditions, including precise developmental staging, are needed for reliable gene expression analyses, in particular in an evolutionary framework.

Evolutionary and ecological functional genomics, from lab to the wild

Plant phenotypes are the result of both genetic and environmental forces that act to modulate trait expression. Over the last few years, numerous approaches in functional genomics and systems biology have led to a greater understanding of plant phenotypic variation and plant responses to the environment. These approaches, and the questions that they can address, have been loosely termed evolutionary and ecological functional genomics (EEFG), and have been providing key insights on how plants adapt and evolve. In particular, by bringing these studies from the laboratory to the field, EEFG studies allow us to gain greater knowledge of how plants function in their natural contexts.

Secondary Wall Regulating NACs Differentially Bind at the Promoter at a CELLULOSE SYNTHASE A4 Cis-eQTL

Arabidopsis thaliana CELLULOSE SYNTHASE A4/7/8 (CESA4/7/8) are three non-redundant subunits of the secondary cell wall cellulose synthase complex. Transcript abundance of these genes can vary among genotypes and expression quantitative trait loci (eQTL) were identified in a recombinant population of the accessions Bay-0 and Shahdara. Genetic mapping and analysis of the transcript levels of CESAs between two distinct near isogenic lines (NILs) confirmed a change in CESA4 expression that segregates within that interval. We sequenced the promoters and identified 16 polymorphisms differentiating CESA4^Sha and CESA4^Bay . In order to determine which of these SNPs could be responsible for this eQTL, we screened for transcription factor protein affinity with promoter fragments of CESA4^Bay, CESA4^Sha , and the reference genome CESA4^Col . The wall thickening activator proteins NAC SECONDARY WALL THICKENING PROMOTING FACTOR2 (NST2) and NST3 exhibited a decrease in binding with the CESA4^Sha promoter with a tracheary element-regulating cis-element (TERE) polymorphism. While NILs harboring the TERE polymorphisms exhibited significantly different CESA4 expression, cellulose crystallinity and cell wall thickness were indistinguishable. These results suggest that the TERE polymorphism resulted in differential transcription factor binding and CESA4 expression; yet A. thaliana is able to tolerate this transcriptional variability without compromising the structural elements of the plant, providing insight into the elasticity of gene regulation as it pertains to cell wall biosynthesis and regulation. We also explored available DNA affinity purification sequencing data to resolve a core binding site, C(G/T)TNNNNNNNA(A/C)G, for secondary wall NACs referred to as the VNS element.

Allele-specific expression and genetic determinants of transcriptomic variations in response to mild water deficit in tomato

Characterizing the natural diversity of gene expression across environments is an important step in understanding how genotype-by-environment interactions shape phenotypes. Here, we analyzed the impact of water deficit onto gene expression levels in tomato at the genome-wide scale. We sequenced the transcriptome of growing leaves and fruit pericarps at cell expansion stage in a cherry and a large fruited accession and their F₁ hybrid grown under two watering regimes. Gene expression levels were steadily affected by the genotype and the watering regime. Whereas phenotypes showed mostly additive inheritance, ~80% of the genes displayed non-additive inheritance. By comparing allele-specific expression (ASE) in the F₁ hybrid to the allelic expression in both parental lines, respectively, 3005 genes in leaf and 2857 genes in fruit deviated from 1:1 ratio independently of the watering regime. Among these genes, ~55% were controlled by cis factors, ~25% by trans factors and ~20% by a combination of both types of factors. A total of 328 genes in leaf and 113 in fruit exhibited significant ASE-by-watering regime interaction, among which ~80% presented trans-by-watering regime interaction, suggesting a response to water deficit mediated through a majority of trans-acting loci in tomato. We cross-validated the expression levels of 274 transcripts in fruit and leaves of 124 recombinant inbred lines (RILs) and identified 163 expression quantitative trait loci (eQTLs) mostly confirming the divergences identified by ASE. Combining phenotypic and expression data, we observed a complex network of variation between genes encoding enzymes involved in the sugar metabolism.

Natural genetic variation shapes root system responses to phytohormones in Arabidopsis

Plants adjust their architecture by modulating organ growth. This ability is largely dependent on phytohormones. While responses to phytohormones have been studied extensively, it remains unclear to which extent and how these responses are modulated in non-reference strains. Here, we assess variation of root traits upon treatment with auxin, cytokinin and abscisic acid (ABA) in 192 Arabidopsis accessions. We identify common response patterns, uncover the extent of their modulation by specific genotypes, and find that the Col-0 reference accession is not a good representative of the species in this regard. We conduct genome-wide association studies and identify 114 significant associations, most of them relating to ABA treatment. The numerous ABA candidate genes are not enriched for known ABA-associated genes, indicating that we largely uncovered unknown players. Overall, our study provides a comprehensive view of the diversity of hormone responses in the Arabidopsis thaliana species, and shows that variation of genes that are yet mostly not associated with such a role to determine natural variation of the response to phytohormones.

Genetics of trans-regulatory variation in gene expression

Heritable variation in gene expression forms a crucial bridge between genomic variation and the biology of many traits. However, most expression quantitative trait loci (eQTLs) remain unidentified. We mapped eQTLs by transcriptome sequencing in 1012 yeast segregants. The resulting eQTLs accounted for over 70% of the heritability of mRNA levels, allowing comprehensive dissection of regulatory variation. Most genes had multiple eQTLs. Most expression variation arose from trans-acting eQTLs distant from their target genes. Nearly all trans-eQTLs clustered at 102 hotspot locations, some of which influenced the expression of thousands of genes. Fine-mapped hotspot regions were enriched for transcription factor genes. While most genes had a local eQTL, most of these had no detectable effects on the expression of other genes in trans. Hundreds of non-additive genetic interactions accounted for small fractions of expression variation. These results reveal the complexity of genetic influences on transcriptome variation in unprecedented depth and detail.

QTLs and eQTLs mapping related to citrandarins' resistance to citrus gummosis disease

Background

Phytophthora nicotianae Breda de Haan (Phytophthora parasitica Dastur) causes severe damage to citrus crops worldwide. A population of citrandarins was created from the cross between the susceptible parent Citrus sunki Hort. Ex Tan. and the resistant parent Poncirus trifoliata (L.) Raf. cv. Rubidoux, both parents and two reference rootstocks (Rangpur lime and Swingle citrumelo) were grafted in a greenhouse on Rangpur lime. Inoculations were performed at 10 cm and 15 cm above the grafting region and the resulting lesions were evaluated by measuring the lesion length 60 days after inoculation. As control, non-inoculated plants of each genotype were used. In addition, we evaluated the expression of 19 candidate genes involved in citrus defense response 48 h after pathogen infection by quantitative Real-Time PCR (qPCR). We mapped genomic regions of Quantitative Trait Loci (QTLs) and Expression Quantitative Trait Loci (eQTLs) associated with resistance to P. parasitica in the linkage groups (LGs) of the previously constructed maps of C. sunki and P. trifoliata.

Results

We found disease severity differences among the generated hybrids, with lesion lengths varying from 1.15 to 11.13 mm. The heritability of the character was 65%. These results indicate that there is a great possibility of success in the selection of resistant hybrids within this experiment. The analysis of gene expression profile demonstrated a great variation of responses regarding the activation of plant defense pathways, indicating that citrandarins have several defense strategies to control oomycete infection. The information of the phenotypic and gene expression data made possible to detect genomic regions associated with resistance. Three QTLs and 84 eQTLs were detected in the linkage map of P. trifoliata, while one QTL and 110 eQTLs were detected in C. sunki.

Conclusions

This is the first study to use eQTLs mapping in the Phytophthora-citrus interaction. Our results from the QTLs and eQTLs mapping allow us to conclude that the resistance of some citrandarins to the infection by P. parasitica is due to a favorable combination of QTLs and eQTLs transmitted by both parents.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4888-2) contains supplementary material, which is available to authorized users.

A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets

BACKGROUND

Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding.

RESULTS

We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions.

CONCLUSIONS

The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO .